ALBERT

Description: The Piton de la Fournaise basaltic volcano, on La Réunion Island in the western Indian Ocean, is one of the most active volcanoes in the world. This volcano is classically considered as the surface expression of an upwelling mantle plume and its activity is continuously monitored, providing detailed information on its superficial dynamics and on the edifice structure. Deeper crustal and upper mantle structure under La Réunion Island is surprisingly poorly constrained, motivating this study. We used receiver function techniques to determine a shear wave velocity profile through the crust and uppermost mantle beneath La Réunion, but also at other seismic stations located on the hotspot track, to investigate the plume and lithosphere interaction and its evolution through time. Receiver functions (RFs) were computed at permanent broad-band seismic stations from the GEOSCOPE network (on La Réunion and Rodrigues), at IRIS stations MRIV and DGAR installed on Mauritius and Diego Garcia islands, and at the GEOFON stations KAAM and HMDM on the Maldives. We performed non-linear inversions of RFs through modelling of P -to- S conversions at various crustal and upper mantle interfaces. Joint inversion of RF and surface wave dispersion data suggests a much deeper Mohorovičić discontinuity (Moho) beneath Mauritius (~21 km) compared to La Réunion (~12 km). A magmatic underplated body may be present under La Réunion as a thin layer (≤3 km thick), as suggested by a previous seismic refraction study, and as a much thicker layer beneath other stations located on the hotspot track, suggesting that underplating is an important process resulting from the plume–lithosphere interaction. We find evidence for a strikingly low velocity layer starting at about 33 km depth beneath La Réunion that we interpret as a zone of partial melt beneath the active volcano. We finally observe low velocities below 70 km beneath La Réunion and below 50 km beneath Mauritius that could represent the base of the oceanic lithosphere.

Description: Intraplate volcanism adjacent to active continental margins is not simply explained by plate tectonics or plume interaction. Recent volcanoes in northeast (NE) Asia, including NE China and the Korean Peninsula, are characterized by heterogeneous tectonic structures and geochemical compositions. Here, we apply a trans-dimensional Bayesian tomography to estimate high-resolution images of group and phase velocity variations (with periods between 8 and 70 s). The method provides robust estimations of velocity maps, and the reliability of results is tested through carefully-designed synthetic recovery experiments. Our maps reveal two sub-lithospheric low velocity anomalies that connect back-arc regions (in Japan and Ryukyu Trench) with current margins of continental lithosphere where the volcanoes are distributed. Combined with evidences from previous geochemical and geophysical studies, we argue that the volcanoes are related to the low velocity structures associated with back-arc processes and pre-existing continental lithosphere.

Description: [1]  Determining the scale-length, magnitude, and distribution of heterogeneity in the lowermost mantle is crucial to understanding whole mantle dynamics, and yet it remains a much debated and ongoing challenge in geophysics. Common shortcomings of current seismically-derived lowermost mantle models are incomplete raypath coverage, arbitrary model parameterization, inaccurate uncertainty estimates, and an ad hoc definition of the misfit function in the optimization framework. In response, we present a new approach to global tomography. Apart from improving the existing raypath coverage using only high quality cross-correlated waveforms, the problem is addressed within a Bayesian framework where explicit regularization of model parameters is notrequired. We obtain high resolution images, complete with uncertainty estimates, of the lowermost mantle P-wave velocity structure using a hand-picked dataset of PKPab-df, PKPbc-df, and PcP-P differential traveltimes. Most importantly, our results demonstrate that the root mean square of the P-wave velocity variations in the lowermost mantle is approximately 0.87%, which is three times larger than previous global-scale estimates.

Description: The origin of the microseismic wavefield is associated with deep ocean and coastal regions where, under certain conditions, ocean waves can excite seismic waves that propagate as surface and body waves. Given that the characteristics of seismic signals generally vary with frequency, here we explore the frequency and azimuth dependent properties of microseisms recorded at a medium aperture (25 km) array in Australia. We examine the frequency dependent properties of the wavefield, and its temporal variation, over two decades (1991–2012), with a focus on relatively high-frequency microseisms (0.325–0.725 Hz) recorded at the Warramunga Array (WRA), which has good slowness resolution capabilities in this frequency range. The analysis is carried out using the Incoherently Averaged Signal (IAS) Capon beamforming, which gives robust estimates of slowness and backazimuth, and is able to resolve multiple wave arrivals within a single time window. For surface waves, we find that fundamental mode Rayleigh waves (R g ) dominate for lower frequencies (〈0.55 Hz) while higher frequencies (〉0.55 Hz) show a transition to higher mode surface waves (L g ). For body waves, source locations are identified in deep ocean regions for lower frequencies and in shallow waters for higher frequencies. We further examine the association between surface wave arrivals and a WAVEWATCH III ocean wave hindcast. Correlations with the ocean wave hindcast show that secondary microseisms in the lower frequency band are generated mainly by ocean swell, while higher frequency bands are generated by the wind sea, i.e. local wind conditions.

Description: Ultra-low velocity zones (ULVZs) are small-scale structures in the Earth's lowermost mantle inferred from the analysis of seismological observations. These structures exhibit a strong decrease in compressional ( P )-wave velocity, shear ( S )-wave velocity, and an increase in density. Quantifying the elastic properties of ULVZs is crucial for understanding their physical origin, which has been hypothesized either as partial melting, iron enrichment, or a combination of the two. Possible disambiguation of these hypotheses can lead to a better understanding of the dynamic processes of the lowermost mantle, such as, percolation, stirring and thermochemical convection. To date, ULVZs have been predominantly studied by forward waveform modelling of seismic waves that sample the core–mantle boundary region. However, ULVZ parameters (i.e. velocity, density, and vertical and lateral extent) obtained through forward modelling are poorly constrained because inferring Earth structure from seismic observations is a non-linear inverse problem with inherent non-uniqueness. To address these issues, we developed a trans-dimensional hierarchical Bayesian inversion that enables rigorous estimation of ULVZ parameter values and their uncertainties, including the effects of model selection. The model selection includes treating the number of layers and the vertical extent of the ULVZ as unknowns. The posterior probability density (solution to the inverse problem) of the ULVZ parameters is estimated by reversible jump Markov chain Monte Carlo sampling that employs parallel tempering to improve efficiency/convergence. First, we apply our method to study the resolution of complex ULVZ structure (including gradually varying structure) by probabilistically inverting simulated noisy waveforms. Then, two data sets sampling the CMB beneath the Philippine and Tasman Seas are considered in the inversion. Our results indicate that both ULVZs are more complex than previously suggested. For the Philippine Sea data, we find a strong decrease in S -wave velocity, which indicates the presence of iron-rich material, albeit this result is accompanied with larger parameter uncertainties than in a previous study. For the Tasman Sea data, our analysis yields a well-constrained S -wave velocity that gradually decreases with depth. We conclude that this ULVZ represents a partial melt of iron-enriched material with higher melt content near its bottom.

Description: SUMMARY A limitation of most forms of passive seismic tomography using distant earthquakes lies in the fact that crustal structure is poorly resolved. An attempt is made here to address this issue by modelling teleseismic receiver functions (RFs) and dispersion curves derived from ambient noise through a multistep approach. The SEAL3 experiment in central and southern New South Wales (NSW) used here, represents one of 13 array deployments that so far comprise the large WOMBAT project, which aims to cover a significant portion of the Australian continent with a rolling array of seismometers. An interactive, forward-modelling software package (IRFFM2) is introduced and applied to the observed RFs and surface wave dispersion curves to define a prior, physically acceptable range of elastic parameters in the lithosphere, which is combined with a grid-search and a linearized inversion. Our results emphasize the importance of a joint treatment of RFs and dispersion data as the predictions from 1-D velocity models at individual stations derived from only RFs display large departures from the observed ambient noise dispersion curves. In total, 27 jointly constrained 1-D shear wave models are produced, which provide sufficient sampling of the crust beneath SEAL3 to permit detailed inferences about lateral variations in structure to be made. Of particular note is the observation that the Moho deepens towards the mountainous southeast, where it exceeds 50 km in depth beneath the Southern Highlands of NSW, thus marking out some of the thickest crust in Australia. The complex lateral variations in mid-lower crustal velocity that we observe probably reflect the manifold interactions of a thinning lithosphere, associated igneous underplating, recent hot-spot-related volcanism and uplift. Our results image an important part of the lithosphere that is poorly constrained by regional and teleseismic tomography, and contribute to the understanding of the formation of the southern highlands and the Palaeozoic Lachlan Orogen.

Description: The problem of decomposing irregular data on the sphere into a set of spherical harmonics is common in many fields of geosciences where it is necessary to build a quantitative understanding of a globally varying field. For example, in global seismology, a compressional or shear wave speed that emerges from tomographic images is used to interpret current state and composition of the mantle, and in geomagnetism, secular variation of magnetic field intensity measured at the surface is studied to better understand the changes in the Earth's core. Optimization methods are widely used for spherical harmonic analysis of irregular data, but they typically do not treat the dependence of the uncertainty estimates on the imposed regularization. This can cause significant difficulties in interpretation, especially when the best-fit model requires more variables as a result of underestimating data noise. Here, with the above limitations in mind, the problem of spherical harmonic expansion of irregular data is treated within the hierarchical Bayesian framework. The hierarchical approach significantly simplifies the problem by removing the need for regularization terms and user-supplied noise estimates. The use of the corrected Akaike Information Criterion for picking the optimal maximum degree of spherical harmonic expansion and the resulting spherical harmonic analyses are first illustrated on a noisy synthetic data set. Subsequently, the method is applied to two global data sets sensitive to the Earth's inner core and lowermost mantle, consisting of PKPab-df and PcP-P differential traveltime residuals relative to a spherically symmetric Earth model. The posterior probability distributions for each spherical harmonic coefficient are calculated via Markov Chain Monte Carlo sampling; the uncertainty obtained for the coefficients thus reflects the noise present in the real data and the imperfections in the spherical harmonic expansion.